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Background/aim: The diagnosis of palmoplantar melanoma is often delayed and misdiagnosis is common, due to frequently unusual clinical presentation. We used a digital dermoscopy analyzer with a series of palmoplantar pigmented skin lesions (PP-PSL), and we compared sensitivity, specificity and diagnostic accuracy obtained with digital dermoscopy analysis (DDA) and classical dermoscopy., Methods: Digital dermoscopy images of 107 PP-PSL were retrospectively obtained from the database of images of 3 Italian centers. The lesions (25 melanomas and 82 nevi) were all removed because of the presence of clinical and/or dermoscopic suspicious features. All digital images were analyzed using appropriate algorithms, and the diagnostic accuracy of the model was calculated. For comparison, dermoscopic images were clinically evaluated by two dermatologists and the Cohen ĸ concordance with DDA was calculated., Results: The stepwise logistic regression analysis selected only 5 parameters out of 49. The logistic model achieved a sensitivity of 96% and a specificity of 87.8%. The Cohen ĸ concordance, evaluated by the Landis and Koch scale, supplied a substantial agreement between dermoscopy and DDA., Conclusions: DDA might be a useful diagnostic instrument in the evaluation of preselected PP-PSL. However, these findings should be confirmed in a formal clinical trial., (Copyright © 2012 S. Karger AG, Basel.)

Background: To date, much confusion exists about the biological significance of dysplastic naevi and about the relationship between melanocytic dysplasia and clinical atypia., Objectives: To use a digital dermoscopy analyser with a series of 'borderline' pigmented skin lesions (i.e. dysplastic naevi and in situ melanomas) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis., Methods: The pigmented skin lesions (n = 174) were histologically examined by three experienced dermatopathologists and identified as in situ melanomas (n = 38) and dysplastic naevi (n = 136). The system evaluated 48 parameters as possible discriminant variables, grouped into four categories: geometry, colours, textures and islands of colour. Once the lesions were analysed (stepwise discriminant analysis), sensitivity, specificity and accuracy were calculated., Results: At the end of the stepwise procedure the percentage of cases classified correctly was 71.8%. Of 136 dysplastic naevi only 98 were classified correctly, while 27 of 38 in situ melanomas were recognized correctly., Conclusions: We conclude that there are so far no digital dermoscopic criteria that can clearly distinguish dysplastic naevi from in situ melanomas.

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions., (Copyright 2002 Wiley-Liss, Inc.)

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

An approach based on the modified Karhunen-Loéve expansion (MKLE) of constitutive and facultative skin colour data acquired by colorimeters in melanoma patients and healthy control subjects, was used to identify two colour features defining skin-colour-associated risk of melanoma. None of four common statistical classifiers trained on colour features were sufficiently accurate for allowing skin colour alone to be used for classification purposes, though a Bayesian quadratic classifier matched the transformed data well. This study supersedes the indeterminate character of most common clinical criteria based on qualitative factors and, irrespective of the results of classification, provides objective skin colour information for the prevention of melanoma.

Objectives: To use a digital dermoscopy analyzer with a series of "borderline" pigmentary skin lesions (ie, clinically atypical nevi and early melanoma) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis., Design: A total of 147 pigmentary skin lesions were histologically examined by 3 experienced dermatopathologists and identified as nevi (n = 90) and melanomas (n = 57). The system evaluated 36 variables to be studied as possible discriminant variables, grouped into 4 categories: geometries, colors, textures, and islands of color., Setting: University medical department., Patients: A sample of patients with excised pigmentary skin lesions (nevi and melanomas)., Main Outcome Measures: Sensitivity, specificity, and accuracy of the model for evaluating "borderline" pigmentary skin lesions., Results: After multivariate stepwise discriminant analysis, only 13 variables were selected to compute the canonical discriminant function., Conclusion: The present method made it possible to determine which objective variables are important for distinguishing atypical benign pigmentary skin lesions and early melanoma.

A statistical procedure to evaluate melanoma risk in Caucasian subjects on the basis of colorimetric measurement of skin colour and Fitzpatrick phototype is described. One hundred and sixty melanoma patients and 546 randomized healthy subjects of similar age, sex and place of origin were examined in the same period for skin colour using a tristimulus colorimeter and for Fitzpatrick phototype. A clinical score for classification purposes was obtained by statistical discriminant analysis with multivariate data transformation and dimension reduction techniques. A Fisher linear classifier was chosen for its simplicity and robustness in correctly predicting melanoma risk in new subjects. The classification rule was designed to avoid classifying subjects at high risk for melanoma as low risk, i.e. to give a negligible number of false negatives at the expense of more false positives. The procedure is objective and readily adapted to different clinical requirements. This is only a preliminary study but it is hoped that by performing more complex statistical analyses, e.g. neural networks, and adding other parameters (proven risk factors such as number of naevi) the performance will be further improved.

Cutaneous melanoma, stage I, from 35 survivors at 5 year follow-up and 16 non-survivors were studied. Mean nuclear area in the superficial layer was significantly larger than in the deep layer both in survivors and non-survivors, but the ratio between nuclear area in superficial and deep layers (so-called maturation index) did not differ between survivors and non-survivors. In comparison with the survivors, the mean nuclear area of non-survivors was significantly larger both in the superficial (51.1 microns2 vs 43.7 microns2, p less than 0.01) and deep (42.9 microns2 vs 36.4 microns2, p less than 0.05) layer. This points to a general increase in nuclear areas in metastasizing tumors. Furthermore, the coefficient of variation of nuclear area [(standard deviation/mean) x 100] was not different between survivors and non-survivors, either in the superficial or in the deep layer. Inspection of histograms of areas of 1000-2000 nuclei per case in 20 random cases (10 survivors and 10 non-survivors) showed a homogeneous increase in nuclear area in non-survivors. None of the histograms revealed a cell clone with especially large nuclei. These data show that the increased mean nuclear area in non-survivors is due to a homogeneous increase of all nuclei throughout the tumor and not to a special cell clone with large nuclei within nuclei of otherwise normal size. The difference in mean nuclear area in superficial and deep layers indicates that careful selection of nuclei in either of these layers is essential to obtain reproducible and comparable results with interactive morphometry.